
import Levenshtein
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import shap
import warnings
import xgboost



from pandarallel import pandarallel
from scipy.stats import spearmanr


# Configuration
pandarallel.initialize(use_memory_fs=False)
warnings.filterwarnings('ignore')

INFO: Pandarallel will run on 6 workers.
INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.


raw_data_path = '../data/raw/'


# You can get this data from: 
#https://www.kaggle.com/datasets/alejopaullier/aminoacids-physical-and-chemical-properties

aminoacids = pd.read_csv(f'{raw_data_path}aminoacids.csv')
aminoacids.rename(
    columns={
        col: col.lower().strip().replace(' ', '_') 
        for col in aminoacids.columns
    }, inplace=True
)


protein_lists_pbd = pd.read_csv(
    f'{raw_data_path}similar_protein_sequences.csv', 
    index_col="seq_id"
)


df_train_updates = pd.read_csv(f"{raw_data_path}train_updates_20220929.csv", index_col="seq_id")
df_train = pd.read_csv(f"{raw_data_path}train.csv", index_col="seq_id")
all_features_nan = df_train_updates.isnull().all("columns")
drop_indices = df_train_updates[all_features_nan].index
df_train = df_train.drop(index=drop_indices)
swap_ph_tm_indices = df_train_updates[~all_features_nan].index
df_train.loc[swap_ph_tm_indices, ["pH", "tm"]] = df_train_updates.loc[swap_ph_tm_indices, ["pH", "tm"]]
df_train = df_train[df_train.protein_sequence.notna()]
df_train = df_train[df_train.pH.notna()]


df_train.head()


def similarity_levenshtein(protein1, protein2):
    distance = Levenshtein.distance(protein1, protein2)
    return distance

def change_positions(protein1, protein2):
    edit_operations = Levenshtein.editops(protein1, protein2)
    result_positions = [element[1] for element in edit_operations]
    return result_positions

def get_protein_similarity_groups(protein_sequence):
    lenght_protein = len(protein_sequence)
    upper_val = lenght_protein + 50
    lower_val = lenght_protein - 50

    filtered_df_train = df_train[
        df_train.protein_len.between(
            lower_val, upper_val
        )
    ]
    protein_seq_similarity_levenshtein = filtered_df_train.protein_sequence.apply(
        lambda protein_sequence2: similarity_levenshtein(
            protein_sequence, protein_sequence2
        )
    )
    levenshtein_list = protein_seq_similarity_levenshtein[
        protein_seq_similarity_levenshtein < 10 # Why 10? 
    ].index.to_list()    
    return levenshtein_list


df_train['protein_len'] = df_train.protein_sequence.str.len()

group_lists = df_train.protein_sequence.parallel_apply(
    get_protein_similarity_groups
)


group_lists_df = pd.DataFrame(group_lists).rename(columns={'protein_sequence': 'group_list'})
group_lists_df['group_list_len'] = group_lists_df.group_list.apply(len)
group_lists_df['group_list_str'] = group_lists_df.group_list.apply(str)
group_lists_df = group_lists_df[group_lists_df.group_list_len > 4]
relevant_groups = {
    gr:f'{i}_group'
    for i, gr in 
    enumerate(
        group_lists_df.group_list_str.unique()
    )
}
group_lists_df['group_str'] = group_lists_df.group_list_str.map(relevant_groups)
df_train.loc[group_lists_df.index, 'group'] = group_lists_df.group_str
reduced_df_train = df_train[df_train.group.notna()]
reduced_df_train['pdb_list'] = protein_lists_pbd.loc[reduced_df_train.index].protein_sequence


def get_paired_mutations(tmp):
    x2 = tmp.protein_sequence.to_list()
    zero_matrix = np.zeros((len(tmp), len(tmp)))
    single_distances_pairs = []
    new_rows = []
    for i, word in enumerate(x2):
        for j, word2 in enumerate(x2):
            distance = similarity_levenshtein(word, word2)
            zero_matrix[i,j] = distance
            if 0 < distance <= 1:
                if i<j:
                    continue
                else:
                    single_distances_pairs.append((i,j))
                    row1 = tmp.iloc[i]
                    row2 = tmp.iloc[j]
                    dd = {}
                    indexes_of_change = change_positions(word, word2)
                    wildtype = word[indexes_of_change[0]]
                    mutation = word2[indexes_of_change[0]]
                    dd['seq1'] = row1.protein_sequence
                    dd['tm1'] = row1.tm
                    dd['ds1'] = row1.data_source
                    dd['pH1'] = row1.pH
                    dd['len1'] = row1.protein_len
                    dd['list1'] = row1.pdb_list
                    dd['seq2'] = row2.protein_sequence
                    dd['tm2'] = row2.tm
                    dd['ds2'] = row2.data_source
                    dd['pH2'] = row2.pH
                    dd['len2'] = row2.protein_len
                    dd['list2'] = row2.pdb_list
                    dd['mutation_type'] = distance
                    dd['mutation'] = mutation
                    dd['wildtype'] = wildtype
                    dd['edit_operations'] = indexes_of_change
                    new_rows.append(dd)
    new_df = pd.DataFrame(new_rows)
    return new_df


paired_df = reduced_df_train.groupby('group').apply(
    get_paired_mutations)


temporal_data = paired_df.copy().reset_index()
temporal_data = temporal_data.drop(
    ['level_1','ds1', 'ds2', 'len2'],
    axis=1
)


temporal_data['mutation_position'] =  temporal_data['edit_operations'].apply(lambda x: x[0])
temporal_data['relative_mutation_position'] = temporal_data['mutation_position']/temporal_data['len1']
temporal_data['d_pH'] = temporal_data['pH2'] - temporal_data['pH1']
temporal_data['d_tm'] = temporal_data['tm2'] - temporal_data['tm1']


def aminoacid_data(row):
    letter_wildtype = row.wildtype[0]
    letter_mutation = row.mutation[0]
    wild_amino = aminoacids[aminoacids.letter == letter_wildtype]
    muta_amino = aminoacids[aminoacids.letter == letter_mutation]
    cols = [
        'molecular_weight',
        'residue_weight',
        'pka1',
        'pkb2',
        'pkx3',
        'pl4',
        'h',
        'vsc',
        'p1',
        'p2',
        'sasa',
        'ncisc',
        'carbon',
        'hydrogen',
        'nitrogen',
        'oxygen',
        'sulfur'
    ]
    difference = (
        muta_amino[cols].reset_index(drop=True).T - 
        wild_amino[cols].reset_index(drop=True).T
    )
    result = dict(difference[0])
    result = {f'{key}__difference': result[key] for key in result.keys()}
    return result


temporal_data_aminoacid_info = pd.json_normalize(
    temporal_data.parallel_apply(
        aminoacid_data, axis=1
    )
)


temporal_data_aminoacid_info.head()


drop_cols = [
    'list1', 
#    'len1',
    'tm2',
    'list2',
    'mutation', 
    'wildtype', 
    'edit_operations',
    'mutation_position',
]


final_train_data = pd.concat([
    temporal_data.drop(drop_cols, axis=1),
    temporal_data_aminoacid_info], axis=1)
final_train_data


def generate_graph(protein_sequence):
    from_to_dict = {
        'from':list(protein_sequence[:-1]),
        'to':list(protein_sequence[1:])
    }
    protein_sequence_directions = pd.DataFrame(from_to_dict)
    list_of_paths = protein_sequence_directions.groupby('from').to.agg(list).values.tolist()
    G=nx.Graph()
    for path in list_of_paths:
        nx.add_path(G, path)
    return G
    
def flatten_result_dict(result_dictionary):
    index = []
    values = []
    for key in result_dictionary.keys():
        for letter in result_dictionary[key].keys():
            index.append(f'{key}_{letter}')
            values.append(result_dictionary[key][letter])
    result = pd.Series(values, index=index)
    return result 

def get_graph_results(graph):
    result_dictionary = {
        'degree_dict':dict(graph.degree()),
        'pagerank_dict': nx.pagerank(graph),
        #'centrality_eigenvector': nx.eigenvector_centrality(graph),
        'centrality_degree': nx.degree_centrality(graph),
        'centrality_closeness': nx.closeness_centrality(graph),
    }
    result_dictionary_df = flatten_result_dict(result_dictionary=result_dictionary)
    return result_dictionary_df


graph_seq1 = final_train_data['seq1'].apply(generate_graph).apply(get_graph_results)
graph_seq2 = final_train_data['seq2'].apply(generate_graph).apply(get_graph_results)


for col in graph_seq1.columns:
    final_train_data[f'{col}_graph_diff'] = (
        graph_seq2[col]-graph_seq1[col]
    )


final_train_data.head()


def aminoacid_window_letter_sum_func(df):
    position = df.relative_mutation_position * len(df.seq1)
    left_position = position - 2
    right_position = position + 2
    aminoacid_window = df.seq1[int(left_position):int(right_position + 1)]
    list_test = []
    for i, letter in enumerate(aminoacid_window):
        if i==2:
            continue
        list_test.append(aminoacids[aminoacids.letter==letter])
    if not list_test:
        return None
    concatenated_letters = pd.concat(list_test)
    total_vecinity = concatenated_letters.select_dtypes('number').sum()
    rival_vecinity = concatenated_letters.select_dtypes('number').head(2).sum()-concatenated_letters.select_dtypes('number').tail(2).sum()
    total_vecinity.rename(index={ix:f'total_sum_neighbour_{ix}' for ix in total_vecinity.index}, inplace=True)
    rival_vecinity.rename(index={ix:f'rival_dif_neighbour_{ix}' for ix in rival_vecinity.index}, inplace=True)
    vecinity_results = total_vecinity.append(rival_vecinity)
    return vecinity_results


vecinity_results = final_train_data.apply(
    aminoacid_window_letter_sum_func, axis=1
)


vecinity_results


final_train_data = pd.concat([final_train_data, vecinity_results], axis=1)


for i in range(3):
    final_train_data[f'random_{i}'] = np.random.randint(0, 1000, len(final_train_data))


unique_groups = final_train_data.group.unique()
test_groups = np.random.choice(
    unique_groups, int(len(unique_groups)*0.3)
)
remaining_groups = list(set(unique_groups) - set(test_groups))
dev_groups = np.random.choice(
    remaining_groups, int(len(unique_groups)*0.2)
)

train_groups = list(set(remaining_groups) - set(dev_groups))

test_data = final_train_data[final_train_data.group.isin(test_groups)]
dev_data = final_train_data[final_train_data.group.isin(dev_groups)]
train_data = final_train_data[final_train_data.group.isin(train_groups)]

non_train_data = ['group', 'd_tm', 'seq1', 'seq2', 'tm1']
X_train, y_train = train_data.drop(non_train_data, axis=1), train_data.d_tm
X_test, y_test = test_data.drop(non_train_data, axis=1), test_data.d_tm
X_dev, y_dev = test_data.drop(non_train_data, axis=1), test_data.d_tm


model = xgboost.XGBRegressor(
    n_estimators=100000,
    max_depth=6,
    learning_rate=0.005,
    subsample=0.8,
    colsample_bytree=0.8,
)

model.fit(
    X_train, 
    abs(y_train), 
    eval_set=[(X_train, abs(y_train)), (X_dev, abs(y_dev))],
    early_stopping_rounds=1000,
    verbose=100
)

[0]	validation_0-rmse:10.22332	validation_1-rmse:11.76717
[100]	validation_0-rmse:7.95783	validation_1-rmse:9.53872
[200]	validation_0-rmse:6.72529	validation_1-rmse:8.31012
[300]	validation_0-rmse:6.04753	validation_1-rmse:7.81668
[400]	validation_0-rmse:5.64977	validation_1-rmse:7.63963
[500]	validation_0-rmse:5.39444	validation_1-rmse:7.57997
[600]	validation_0-rmse:5.20831	validation_1-rmse:7.56677
[700]	validation_0-rmse:5.06867	validation_1-rmse:7.56918
[800]	validation_0-rmse:4.94695	validation_1-rmse:7.57112
[900]	validation_0-rmse:4.84597	validation_1-rmse:7.57166
[1000]	validation_0-rmse:4.74744	validation_1-rmse:7.57240
[1100]	validation_0-rmse:4.65609	validation_1-rmse:7.57299
[1200]	validation_0-rmse:4.56744	validation_1-rmse:7.58014
[1300]	validation_0-rmse:4.48359	validation_1-rmse:7.58500
[1400]	validation_0-rmse:4.40113	validation_1-rmse:7.59118
[1500]	validation_0-rmse:4.32576	validation_1-rmse:7.59690
[1563]	validation_0-rmse:4.27873	validation_1-rmse:7.59800

XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.8,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,
             grow_policy='depthwise', importance_type=None,
             interaction_constraints='', learning_rate=0.005, max_bin=256,
             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,
             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=100000, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, ...)

XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.8,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,
             grow_policy='depthwise', importance_type=None,
             interaction_constraints='', learning_rate=0.005, max_bin=256,
             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,
             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=100000, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, ...)


plt.scatter(
    y_test,
    model.predict(X_test),
    s=0.1
)
plt.plot([-40,40], [-40,40])

[<matplotlib.lines.Line2D at 0x7ffaea23a460>]


spearmanr(
    y_test,
    model.predict(X_test)
)

SpearmanrResult(correlation=-0.10056908019545979, pvalue=3.568838627307488e-17)


feature_importances_df = pd.Series(
    model.feature_importances_, index=X_test.columns
).sort_values()

plt.figure(figsize=(10,30))
plt.barh(feature_importances_df.index,feature_importances_df)

<BarContainer object of 140 artists>


explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)


shap.summary_plot(shap_values, X_test)


top_inds = np.argsort(-np.sum(np.abs(shap_values), 0))

for i in range(20):
    shap.dependence_plot(top_inds[i], shap_values, X_test)


model = xgboost.XGBClassifier(
    n_estimators=100000,
    max_depth=4,
    learning_rate=0.001,
    subsample=0.6,
    colsample_bytree=0.2,
)

model.fit(
    X_train, 
    y_train > 0, 
    eval_set=[(X_train, y_train > 0), (X_dev, y_dev > 0)],
    eval_metric=['auc', 'logloss'],
    early_stopping_rounds=1000,
)

[0]	validation_0-auc:0.66316	validation_0-logloss:0.69303	validation_1-auc:0.50717	validation_1-logloss:0.69312
[1]	validation_0-auc:0.71048	validation_0-logloss:0.69291	validation_1-auc:0.58082	validation_1-logloss:0.69304
[2]	validation_0-auc:0.72107	validation_0-logloss:0.69278	validation_1-auc:0.57919	validation_1-logloss:0.69298
[3]	validation_0-auc:0.72567	validation_0-logloss:0.69269	validation_1-auc:0.57909	validation_1-logloss:0.69297
[4]	validation_0-auc:0.73257	validation_0-logloss:0.69258	validation_1-auc:0.61402	validation_1-logloss:0.69289
[5]	validation_0-auc:0.73858	validation_0-logloss:0.69247	validation_1-auc:0.61808	validation_1-logloss:0.69283
[6]	validation_0-auc:0.74558	validation_0-logloss:0.69235	validation_1-auc:0.62422	validation_1-logloss:0.69278
[7]	validation_0-auc:0.75309	validation_0-logloss:0.69222	validation_1-auc:0.62226	validation_1-logloss:0.69273
[8]	validation_0-auc:0.75609	validation_0-logloss:0.69208	validation_1-auc:0.61945	validation_1-logloss:0.69269
[9]	validation_0-auc:0.75774	validation_0-logloss:0.69197	validation_1-auc:0.62272	validation_1-logloss:0.69262
[10]	validation_0-auc:0.75912	validation_0-logloss:0.69184	validation_1-auc:0.61977	validation_1-logloss:0.69257
[11]	validation_0-auc:0.75847	validation_0-logloss:0.69173	validation_1-auc:0.61833	validation_1-logloss:0.69252
[12]	validation_0-auc:0.75892	validation_0-logloss:0.69162	validation_1-auc:0.61617	validation_1-logloss:0.69248
[13]	validation_0-auc:0.75637	validation_0-logloss:0.69154	validation_1-auc:0.61475	validation_1-logloss:0.69244
[14]	validation_0-auc:0.75750	validation_0-logloss:0.69142	validation_1-auc:0.61369	validation_1-logloss:0.69243
[15]	validation_0-auc:0.75778	validation_0-logloss:0.69128	validation_1-auc:0.61310	validation_1-logloss:0.69238
[16]	validation_0-auc:0.75798	validation_0-logloss:0.69115	validation_1-auc:0.61780	validation_1-logloss:0.69233
[17]	validation_0-auc:0.75852	validation_0-logloss:0.69103	validation_1-auc:0.61309	validation_1-logloss:0.69230
[18]	validation_0-auc:0.75840	validation_0-logloss:0.69093	validation_1-auc:0.60997	validation_1-logloss:0.69228
[19]	validation_0-auc:0.75832	validation_0-logloss:0.69081	validation_1-auc:0.60801	validation_1-logloss:0.69224
[20]	validation_0-auc:0.75761	validation_0-logloss:0.69068	validation_1-auc:0.60865	validation_1-logloss:0.69218
[21]	validation_0-auc:0.75855	validation_0-logloss:0.69060	validation_1-auc:0.60640	validation_1-logloss:0.69216
[22]	validation_0-auc:0.75962	validation_0-logloss:0.69049	validation_1-auc:0.60771	validation_1-logloss:0.69215
[23]	validation_0-auc:0.75868	validation_0-logloss:0.69040	validation_1-auc:0.60559	validation_1-logloss:0.69211
[24]	validation_0-auc:0.75811	validation_0-logloss:0.69029	validation_1-auc:0.60769	validation_1-logloss:0.69206
[25]	validation_0-auc:0.75895	validation_0-logloss:0.69020	validation_1-auc:0.60787	validation_1-logloss:0.69203
[26]	validation_0-auc:0.76034	validation_0-logloss:0.69011	validation_1-auc:0.60611	validation_1-logloss:0.69201
[27]	validation_0-auc:0.76059	validation_0-logloss:0.69003	validation_1-auc:0.60765	validation_1-logloss:0.69198
[28]	validation_0-auc:0.76018	validation_0-logloss:0.68995	validation_1-auc:0.60592	validation_1-logloss:0.69196
[29]	validation_0-auc:0.76037	validation_0-logloss:0.68984	validation_1-auc:0.60585	validation_1-logloss:0.69193
[30]	validation_0-auc:0.76005	validation_0-logloss:0.68972	validation_1-auc:0.60462	validation_1-logloss:0.69190
[31]	validation_0-auc:0.76067	validation_0-logloss:0.68963	validation_1-auc:0.60695	validation_1-logloss:0.69186
[32]	validation_0-auc:0.76053	validation_0-logloss:0.68951	validation_1-auc:0.60929	validation_1-logloss:0.69178
[33]	validation_0-auc:0.76190	validation_0-logloss:0.68937	validation_1-auc:0.61522	validation_1-logloss:0.69169
[34]	validation_0-auc:0.76268	validation_0-logloss:0.68926	validation_1-auc:0.61329	validation_1-logloss:0.69167
[35]	validation_0-auc:0.76249	validation_0-logloss:0.68913	validation_1-auc:0.61265	validation_1-logloss:0.69163
[36]	validation_0-auc:0.76289	validation_0-logloss:0.68900	validation_1-auc:0.61120	validation_1-logloss:0.69159
[37]	validation_0-auc:0.76244	validation_0-logloss:0.68888	validation_1-auc:0.61097	validation_1-logloss:0.69155
[38]	validation_0-auc:0.76239	validation_0-logloss:0.68877	validation_1-auc:0.61159	validation_1-logloss:0.69149
[39]	validation_0-auc:0.76250	validation_0-logloss:0.68867	validation_1-auc:0.61100	validation_1-logloss:0.69144
[40]	validation_0-auc:0.76281	validation_0-logloss:0.68853	validation_1-auc:0.61122	validation_1-logloss:0.69140
[41]	validation_0-auc:0.76217	validation_0-logloss:0.68844	validation_1-auc:0.61153	validation_1-logloss:0.69136
[42]	validation_0-auc:0.76137	validation_0-logloss:0.68833	validation_1-auc:0.61092	validation_1-logloss:0.69131
[43]	validation_0-auc:0.76112	validation_0-logloss:0.68820	validation_1-auc:0.61164	validation_1-logloss:0.69126
[44]	validation_0-auc:0.76152	validation_0-logloss:0.68807	validation_1-auc:0.61318	validation_1-logloss:0.69119
[45]	validation_0-auc:0.76163	validation_0-logloss:0.68799	validation_1-auc:0.61378	validation_1-logloss:0.69116
[46]	validation_0-auc:0.76141	validation_0-logloss:0.68791	validation_1-auc:0.61297	validation_1-logloss:0.69114
[47]	validation_0-auc:0.76177	validation_0-logloss:0.68783	validation_1-auc:0.61266	validation_1-logloss:0.69112
[48]	validation_0-auc:0.76189	validation_0-logloss:0.68769	validation_1-auc:0.61523	validation_1-logloss:0.69103
[49]	validation_0-auc:0.76132	validation_0-logloss:0.68757	validation_1-auc:0.61444	validation_1-logloss:0.69098
[50]	validation_0-auc:0.76118	validation_0-logloss:0.68748	validation_1-auc:0.61333	validation_1-logloss:0.69096
[51]	validation_0-auc:0.76162	validation_0-logloss:0.68736	validation_1-auc:0.61218	validation_1-logloss:0.69093
[52]	validation_0-auc:0.76152	validation_0-logloss:0.68724	validation_1-auc:0.61073	validation_1-logloss:0.69090
[53]	validation_0-auc:0.76195	validation_0-logloss:0.68710	validation_1-auc:0.60973	validation_1-logloss:0.69086
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[13024]	validation_0-auc:0.90957	validation_0-logloss:0.44246	validation_1-auc:0.67566	validation_1-logloss:0.63354
[13025]	validation_0-auc:0.90957	validation_0-logloss:0.44245	validation_1-auc:0.67566	validation_1-logloss:0.63354
[13026]	validation_0-auc:0.90958	validation_0-logloss:0.44245	validation_1-auc:0.67566	validation_1-logloss:0.63354
[13027]	validation_0-auc:0.90958	validation_0-logloss:0.44244	validation_1-auc:0.67566	validation_1-logloss:0.63354
[13028]	validation_0-auc:0.90958	validation_0-logloss:0.44244	validation_1-auc:0.67565	validation_1-logloss:0.63354
[13029]	validation_0-auc:0.90959	validation_0-logloss:0.44243	validation_1-auc:0.67565	validation_1-logloss:0.63354
[13030]	validation_0-auc:0.90960	validation_0-logloss:0.44242	validation_1-auc:0.67565	validation_1-logloss:0.63354
[13031]	validation_0-auc:0.90960	validation_0-logloss:0.44241	validation_1-auc:0.67563	validation_1-logloss:0.63356
[13032]	validation_0-auc:0.90960	validation_0-logloss:0.44240	validation_1-auc:0.67563	validation_1-logloss:0.63355
[13033]	validation_0-auc:0.90960	validation_0-logloss:0.44239	validation_1-auc:0.67562	validation_1-logloss:0.63356
[13034]	validation_0-auc:0.90960	validation_0-logloss:0.44238	validation_1-auc:0.67561	validation_1-logloss:0.63356
[13035]	validation_0-auc:0.90960	validation_0-logloss:0.44238	validation_1-auc:0.67560	validation_1-logloss:0.63357
[13036]	validation_0-auc:0.90960	validation_0-logloss:0.44237	validation_1-auc:0.67563	validation_1-logloss:0.63355
[13037]	validation_0-auc:0.90961	validation_0-logloss:0.44236	validation_1-auc:0.67562	validation_1-logloss:0.63355
[13038]	validation_0-auc:0.90961	validation_0-logloss:0.44235	validation_1-auc:0.67563	validation_1-logloss:0.63355
[13039]	validation_0-auc:0.90962	validation_0-logloss:0.44234	validation_1-auc:0.67563	validation_1-logloss:0.63355
[13040]	validation_0-auc:0.90963	validation_0-logloss:0.44233	validation_1-auc:0.67565	validation_1-logloss:0.63354
[13041]	validation_0-auc:0.90963	validation_0-logloss:0.44232	validation_1-auc:0.67566	validation_1-logloss:0.63353
[13042]	validation_0-auc:0.90964	validation_0-logloss:0.44231	validation_1-auc:0.67563	validation_1-logloss:0.63355
[13043]	validation_0-auc:0.90965	validation_0-logloss:0.44230	validation_1-auc:0.67564	validation_1-logloss:0.63355
[13044]	validation_0-auc:0.90965	validation_0-logloss:0.44230	validation_1-auc:0.67565	validation_1-logloss:0.63355
[13045]	validation_0-auc:0.90966	validation_0-logloss:0.44228	validation_1-auc:0.67574	validation_1-logloss:0.63350
[13046]	validation_0-auc:0.90966	validation_0-logloss:0.44227	validation_1-auc:0.67575	validation_1-logloss:0.63349
[13047]	validation_0-auc:0.90967	validation_0-logloss:0.44227	validation_1-auc:0.67571	validation_1-logloss:0.63351
[13048]	validation_0-auc:0.90968	validation_0-logloss:0.44226	validation_1-auc:0.67576	validation_1-logloss:0.63348
[13049]	validation_0-auc:0.90968	validation_0-logloss:0.44225	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13050]	validation_0-auc:0.90968	validation_0-logloss:0.44225	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13051]	validation_0-auc:0.90969	validation_0-logloss:0.44224	validation_1-auc:0.67576	validation_1-logloss:0.63349
[13052]	validation_0-auc:0.90970	validation_0-logloss:0.44223	validation_1-auc:0.67576	validation_1-logloss:0.63349
[13053]	validation_0-auc:0.90970	validation_0-logloss:0.44222	validation_1-auc:0.67576	validation_1-logloss:0.63348
[13054]	validation_0-auc:0.90971	validation_0-logloss:0.44222	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13055]	validation_0-auc:0.90971	validation_0-logloss:0.44221	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13056]	validation_0-auc:0.90972	validation_0-logloss:0.44221	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13057]	validation_0-auc:0.90972	validation_0-logloss:0.44220	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13058]	validation_0-auc:0.90972	validation_0-logloss:0.44219	validation_1-auc:0.67578	validation_1-logloss:0.63348
[13059]	validation_0-auc:0.90972	validation_0-logloss:0.44219	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13060]	validation_0-auc:0.90973	validation_0-logloss:0.44218	validation_1-auc:0.67578	validation_1-logloss:0.63348
[13061]	validation_0-auc:0.90973	validation_0-logloss:0.44217	validation_1-auc:0.67580	validation_1-logloss:0.63347
[13062]	validation_0-auc:0.90974	validation_0-logloss:0.44216	validation_1-auc:0.67580	validation_1-logloss:0.63347
[13063]	validation_0-auc:0.90975	validation_0-logloss:0.44215	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13064]	validation_0-auc:0.90975	validation_0-logloss:0.44215	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13065]	validation_0-auc:0.90976	validation_0-logloss:0.44214	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13066]	validation_0-auc:0.90976	validation_0-logloss:0.44214	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13067]	validation_0-auc:0.90976	validation_0-logloss:0.44213	validation_1-auc:0.67583	validation_1-logloss:0.63345
[13068]	validation_0-auc:0.90976	validation_0-logloss:0.44212	validation_1-auc:0.67586	validation_1-logloss:0.63344
[13069]	validation_0-auc:0.90976	validation_0-logloss:0.44212	validation_1-auc:0.67586	validation_1-logloss:0.63344
[13070]	validation_0-auc:0.90977	validation_0-logloss:0.44211	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13071]	validation_0-auc:0.90978	validation_0-logloss:0.44210	validation_1-auc:0.67583	validation_1-logloss:0.63345
[13072]	validation_0-auc:0.90979	validation_0-logloss:0.44210	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13073]	validation_0-auc:0.90979	validation_0-logloss:0.44208	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13074]	validation_0-auc:0.90980	validation_0-logloss:0.44208	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13075]	validation_0-auc:0.90981	validation_0-logloss:0.44207	validation_1-auc:0.67585	validation_1-logloss:0.63344
[13076]	validation_0-auc:0.90981	validation_0-logloss:0.44206	validation_1-auc:0.67585	validation_1-logloss:0.63344
[13077]	validation_0-auc:0.90982	validation_0-logloss:0.44205	validation_1-auc:0.67581	validation_1-logloss:0.63346
[13078]	validation_0-auc:0.90982	validation_0-logloss:0.44204	validation_1-auc:0.67581	validation_1-logloss:0.63347
[13079]	validation_0-auc:0.90983	validation_0-logloss:0.44203	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13080]	validation_0-auc:0.90983	validation_0-logloss:0.44202	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13081]	validation_0-auc:0.90985	validation_0-logloss:0.44201	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13082]	validation_0-auc:0.90985	validation_0-logloss:0.44201	validation_1-auc:0.67581	validation_1-logloss:0.63347
[13083]	validation_0-auc:0.90986	validation_0-logloss:0.44201	validation_1-auc:0.67581	validation_1-logloss:0.63347
[13084]	validation_0-auc:0.90987	validation_0-logloss:0.44200	validation_1-auc:0.67581	validation_1-logloss:0.63347
[13085]	validation_0-auc:0.90987	validation_0-logloss:0.44199	validation_1-auc:0.67581	validation_1-logloss:0.63347
[13086]	validation_0-auc:0.90988	validation_0-logloss:0.44198	validation_1-auc:0.67580	validation_1-logloss:0.63347
[13087]	validation_0-auc:0.90989	validation_0-logloss:0.44197	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13088]	validation_0-auc:0.90989	validation_0-logloss:0.44196	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13089]	validation_0-auc:0.90990	validation_0-logloss:0.44195	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13090]	validation_0-auc:0.90990	validation_0-logloss:0.44195	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13091]	validation_0-auc:0.90990	validation_0-logloss:0.44194	validation_1-auc:0.67578	validation_1-logloss:0.63348
[13092]	validation_0-auc:0.90991	validation_0-logloss:0.44193	validation_1-auc:0.67577	validation_1-logloss:0.63348
[13093]	validation_0-auc:0.90991	validation_0-logloss:0.44192	validation_1-auc:0.67575	validation_1-logloss:0.63350
[13094]	validation_0-auc:0.90991	validation_0-logloss:0.44191	validation_1-auc:0.67570	validation_1-logloss:0.63353
[13095]	validation_0-auc:0.90991	validation_0-logloss:0.44191	validation_1-auc:0.67570	validation_1-logloss:0.63353
[13096]	validation_0-auc:0.90991	validation_0-logloss:0.44190	validation_1-auc:0.67570	validation_1-logloss:0.63353
[13097]	validation_0-auc:0.90992	validation_0-logloss:0.44189	validation_1-auc:0.67569	validation_1-logloss:0.63353
[13098]	validation_0-auc:0.90993	validation_0-logloss:0.44188	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13099]	validation_0-auc:0.90994	validation_0-logloss:0.44187	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13100]	validation_0-auc:0.90995	validation_0-logloss:0.44186	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13101]	validation_0-auc:0.90994	validation_0-logloss:0.44186	validation_1-auc:0.67581	validation_1-logloss:0.63348
[13102]	validation_0-auc:0.90995	validation_0-logloss:0.44185	validation_1-auc:0.67583	validation_1-logloss:0.63347
[13103]	validation_0-auc:0.90996	validation_0-logloss:0.44184	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13104]	validation_0-auc:0.90997	validation_0-logloss:0.44183	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13105]	validation_0-auc:0.90997	validation_0-logloss:0.44183	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13106]	validation_0-auc:0.90997	validation_0-logloss:0.44182	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13107]	validation_0-auc:0.90997	validation_0-logloss:0.44181	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13108]	validation_0-auc:0.90998	validation_0-logloss:0.44180	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13109]	validation_0-auc:0.90999	validation_0-logloss:0.44179	validation_1-auc:0.67576	validation_1-logloss:0.63350
[13110]	validation_0-auc:0.90999	validation_0-logloss:0.44179	validation_1-auc:0.67577	validation_1-logloss:0.63349
[13111]	validation_0-auc:0.91000	validation_0-logloss:0.44178	validation_1-auc:0.67576	validation_1-logloss:0.63350
[13112]	validation_0-auc:0.91000	validation_0-logloss:0.44177	validation_1-auc:0.67576	validation_1-logloss:0.63350
[13113]	validation_0-auc:0.91001	validation_0-logloss:0.44177	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13114]	validation_0-auc:0.91001	validation_0-logloss:0.44175	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13115]	validation_0-auc:0.91002	validation_0-logloss:0.44174	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13116]	validation_0-auc:0.91002	validation_0-logloss:0.44173	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13117]	validation_0-auc:0.91003	validation_0-logloss:0.44173	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13118]	validation_0-auc:0.91003	validation_0-logloss:0.44172	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13119]	validation_0-auc:0.91004	validation_0-logloss:0.44171	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13120]	validation_0-auc:0.91004	validation_0-logloss:0.44171	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13121]	validation_0-auc:0.91005	validation_0-logloss:0.44170	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13122]	validation_0-auc:0.91005	validation_0-logloss:0.44168	validation_1-auc:0.67586	validation_1-logloss:0.63345
[13123]	validation_0-auc:0.91005	validation_0-logloss:0.44168	validation_1-auc:0.67586	validation_1-logloss:0.63344
[13124]	validation_0-auc:0.91006	validation_0-logloss:0.44167	validation_1-auc:0.67585	validation_1-logloss:0.63345
[13125]	validation_0-auc:0.91006	validation_0-logloss:0.44167	validation_1-auc:0.67585	validation_1-logloss:0.63345
[13126]	validation_0-auc:0.91006	validation_0-logloss:0.44166	validation_1-auc:0.67585	validation_1-logloss:0.63345
[13127]	validation_0-auc:0.91008	validation_0-logloss:0.44165	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13128]	validation_0-auc:0.91008	validation_0-logloss:0.44164	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13129]	validation_0-auc:0.91009	validation_0-logloss:0.44163	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13130]	validation_0-auc:0.91009	validation_0-logloss:0.44163	validation_1-auc:0.67588	validation_1-logloss:0.63343
[13131]	validation_0-auc:0.91009	validation_0-logloss:0.44162	validation_1-auc:0.67588	validation_1-logloss:0.63343
[13132]	validation_0-auc:0.91009	validation_0-logloss:0.44162	validation_1-auc:0.67586	validation_1-logloss:0.63344
[13133]	validation_0-auc:0.91010	validation_0-logloss:0.44161	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13134]	validation_0-auc:0.91011	validation_0-logloss:0.44160	validation_1-auc:0.67588	validation_1-logloss:0.63344
[13135]	validation_0-auc:0.91011	validation_0-logloss:0.44160	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13136]	validation_0-auc:0.91011	validation_0-logloss:0.44158	validation_1-auc:0.67596	validation_1-logloss:0.63339
[13137]	validation_0-auc:0.91012	validation_0-logloss:0.44157	validation_1-auc:0.67596	validation_1-logloss:0.63339
[13138]	validation_0-auc:0.91012	validation_0-logloss:0.44156	validation_1-auc:0.67597	validation_1-logloss:0.63339
[13139]	validation_0-auc:0.91012	validation_0-logloss:0.44156	validation_1-auc:0.67597	validation_1-logloss:0.63339
[13140]	validation_0-auc:0.91012	validation_0-logloss:0.44155	validation_1-auc:0.67597	validation_1-logloss:0.63338
[13141]	validation_0-auc:0.91013	validation_0-logloss:0.44155	validation_1-auc:0.67598	validation_1-logloss:0.63338
[13142]	validation_0-auc:0.91013	validation_0-logloss:0.44154	validation_1-auc:0.67598	validation_1-logloss:0.63339
[13143]	validation_0-auc:0.91013	validation_0-logloss:0.44153	validation_1-auc:0.67599	validation_1-logloss:0.63338
[13144]	validation_0-auc:0.91014	validation_0-logloss:0.44152	validation_1-auc:0.67594	validation_1-logloss:0.63340
[13145]	validation_0-auc:0.91015	validation_0-logloss:0.44150	validation_1-auc:0.67598	validation_1-logloss:0.63338
[13146]	validation_0-auc:0.91016	validation_0-logloss:0.44150	validation_1-auc:0.67597	validation_1-logloss:0.63339
[13147]	validation_0-auc:0.91016	validation_0-logloss:0.44149	validation_1-auc:0.67599	validation_1-logloss:0.63338
[13148]	validation_0-auc:0.91016	validation_0-logloss:0.44148	validation_1-auc:0.67600	validation_1-logloss:0.63338
[13149]	validation_0-auc:0.91016	validation_0-logloss:0.44148	validation_1-auc:0.67599	validation_1-logloss:0.63338
[13150]	validation_0-auc:0.91017	validation_0-logloss:0.44147	validation_1-auc:0.67601	validation_1-logloss:0.63337
[13151]	validation_0-auc:0.91018	validation_0-logloss:0.44145	validation_1-auc:0.67589	validation_1-logloss:0.63345
[13152]	validation_0-auc:0.91018	validation_0-logloss:0.44145	validation_1-auc:0.67582	validation_1-logloss:0.63349
[13153]	validation_0-auc:0.91018	validation_0-logloss:0.44144	validation_1-auc:0.67582	validation_1-logloss:0.63348
[13154]	validation_0-auc:0.91018	validation_0-logloss:0.44144	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13155]	validation_0-auc:0.91019	validation_0-logloss:0.44143	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13156]	validation_0-auc:0.91020	validation_0-logloss:0.44143	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13157]	validation_0-auc:0.91020	validation_0-logloss:0.44142	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13158]	validation_0-auc:0.91021	validation_0-logloss:0.44141	validation_1-auc:0.67584	validation_1-logloss:0.63347
[13159]	validation_0-auc:0.91021	validation_0-logloss:0.44141	validation_1-auc:0.67584	validation_1-logloss:0.63347
[13160]	validation_0-auc:0.91022	validation_0-logloss:0.44141	validation_1-auc:0.67584	validation_1-logloss:0.63347
[13161]	validation_0-auc:0.91022	validation_0-logloss:0.44140	validation_1-auc:0.67583	validation_1-logloss:0.63347
[13162]	validation_0-auc:0.91022	validation_0-logloss:0.44139	validation_1-auc:0.67583	validation_1-logloss:0.63347
[13163]	validation_0-auc:0.91023	validation_0-logloss:0.44138	validation_1-auc:0.67585	validation_1-logloss:0.63346

XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.2,
              early_stopping_rounds=None, enable_categorical=False,
              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,
              grow_policy='depthwise', importance_type=None,
              interaction_constraints='', learning_rate=0.001, max_bin=256,
              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,
              max_depth=4, max_leaves=0, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=100000, n_jobs=0,
              num_parallel_tree=1, predictor='auto', random_state=0, ...)

XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.2,
              early_stopping_rounds=None, enable_categorical=False,
              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,
              grow_policy='depthwise', importance_type=None,
              interaction_constraints='', learning_rate=0.001, max_bin=256,
              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,
              max_depth=4, max_leaves=0, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=100000, n_jobs=0,
              num_parallel_tree=1, predictor='auto', random_state=0, ...)


explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)


top_inds = np.argsort(-np.sum(np.abs(shap_values), 0))

for i in range(20):
    shap.dependence_plot(top_inds[i], shap_values, X_test)

	protein_sequence	pH	data_source	tm
seq_id
0	AAAAKAAALALLGEAPEVVDIWLPAGWRQPFRVFRLERKGDGVLVG...	7.0	doi.org/10.1038/s41592-020-0801-4	75.7
1	AAADGEPLHNEEERAGAGQVGRSLPQESEEQRTGSRPRRRRDLGSR...	7.0	doi.org/10.1038/s41592-020-0801-4	50.5
2	AAAFSTPRATSYRILSSAGSGSTRADAPQVRRLHTTRDLLAKDYYA...	7.0	doi.org/10.1038/s41592-020-0801-4	40.5
3	AAASGLRTAIPAQPLRHLLQPAPRPCLRPFGLLSVRAGSARRSGLL...	7.0	doi.org/10.1038/s41592-020-0801-4	47.2
4	AAATKSGPRRQSQGASVRTFTPFYFLVEPVDTLSVRGSSVILNCSA...	7.0	doi.org/10.1038/s41592-020-0801-4	49.5

	molecular_weight__difference	residue_weight__difference	pka1__difference	pkb2__difference	pkx3__difference	pl4__difference	h__difference	vsc__difference	p1__difference	p2__difference	sasa__difference	ncisc__difference	carbon__difference	hydrogen__difference	nitrogen__difference	oxygen__difference
0	-42.08	-42.08	-0.02	0.09	NaN	0.02	-0.44	-66.0	3.2	-0.140	-0.750	-0.044485	-3.0	-6.0	0.0	0.0
1	-30.02	-30.03	0.25	0.59	NaN	0.40	0.67	-23.8	-0.5	-0.062	-0.344	0.003835	-1.0	-2.0	0.0	-1.0
2	-57.05	-57.05	0.17	0.56	NaN	0.35	1.47	-53.2	-2.4	-0.134	-0.751	-0.042024	-2.0	-3.0	-1.0	-1.0
3	14.03	14.03	0.00	0.09	NaN	0.03	0.14	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0
4	14.03	14.03	0.00	0.09	NaN	0.03	0.14	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0

	group	seq1	tm1	pH1	len1	seq2	pH2	mutation_type	relative_mutation_position	d_pH	...	vsc__difference	p1__difference	p2__difference	sasa__difference	ncisc__difference	carbon__difference	hydrogen__difference	nitrogen__difference	oxygen__difference	sulfur__difference
0	0_group	MKGMSKMLQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	67.3	7.5	53.0	MKGMSKMAQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.132075	0.0	...	-66.0	3.2	-0.140	-0.750	-0.044485	-3.0	-6.0	0.0	0.0	0.0
1	0_group	MKGMSKMPQFNLRWPREVLDLVRTVAEENGRSVNSEIYQRVMESFK...	44.0	7.5	53.0	MKGMSKMPQFNLRWPREVLDLVRAVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.433962	0.0	...	-23.8	-0.5	-0.062	-0.344	0.003835	-1.0	-2.0	0.0	-1.0	0.0
2	0_group	MKGMSKMPQFNLRWPREVQDLVRKVAEENGRSVNSEIYQRVMESFK...	35.0	7.5	53.0	MKGMSKMPQFNLRWPREVADLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.339623	0.0	...	-53.2	-2.4	-0.134	-0.751	-0.042024	-2.0	-3.0	-1.0	-1.0	0.0
3	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	0.0	...	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0	0.0
4	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	0.0	...	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11665	93_group	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	52.3	7.5	458.0	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	7.5	1.0	0.788210	0.0	...	-66.0	3.2	-0.140	-0.750	-0.044485	-3.0	-6.0	0.0	0.0	0.0
11666	93_group	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	48.6	7.5	458.0	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	7.5	1.0	0.340611	0.0	...	29.4	1.9	0.072	0.407	0.045859	1.0	1.0	1.0	0.0	0.0
11667	94_group	QDWLTFQKKHITNTRDVDCDNIMSTNLTHCKDKNTFIYSRPEPVKA...	77.4	5.5	104.0	QDWLTFQKKHITNTRDVDCDNIMSTNLAHCKDKNTFIYSRPEPVKA...	5.5	1.0	0.259615	0.0	...	-23.8	-0.5	-0.062	-0.344	0.003835	-1.0	-2.0	0.0	-1.0	0.0
11668	95_group	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	59.2	2.5	68.0	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	2.5	1.0	0.720588	0.0	...	-72.5	-3.2	-0.173	-1.077	-0.010521	-3.0	-7.0	-1.0	0.0	0.0
11669	95_group	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	67.5	2.5	68.0	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	2.5	1.0	0.750000	0.0	...	-53.2	-2.4	-0.134	-0.751	-0.042024	-2.0	-3.0	-1.0	-1.0	0.0

	group	seq1	tm1	pH1	len1	seq2	pH2	mutation_type	relative_mutation_position	...	centrality_degree_T_graph_diff	centrality_closeness_T_graph_diff	degree_dict_C_graph_diff	degree_dict_H_graph_diff	pagerank_dict_C_graph_diff	pagerank_dict_H_graph_diff	centrality_degree_C_graph_diff	centrality_degree_H_graph_diff	centrality_closeness_C_graph_diff	centrality_closeness_H_graph_diff
0	0_group	MKGMSKMLQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	67.3	7.5	53.0	MKGMSKMAQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.132075	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	0_group	MKGMSKMPQFNLRWPREVLDLVRTVAEENGRSVNSEIYQRVMESFK...	44.0	7.5	53.0	MKGMSKMPQFNLRWPREVLDLVRAVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.433962	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	0_group	MKGMSKMPQFNLRWPREVQDLVRKVAEENGRSVNSEIYQRVMESFK...	35.0	7.5	53.0	MKGMSKMPQFNLRWPREVADLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.339623	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	...	0.0	0.0	0.0	0.0	0.000667	0.000124	0.0	0.0	0.0	0.0
4	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	...	0.0	0.0	0.0	0.0	0.000667	0.000124	0.0	0.0	0.0	0.0

	total_sum_neighbour_molecular_weight	total_sum_neighbour_residue_weight	total_sum_neighbour_pka1	total_sum_neighbour_pkb2	total_sum_neighbour_pkx3	total_sum_neighbour_pl4	total_sum_neighbour_h	total_sum_neighbour_vsc	total_sum_neighbour_p1	total_sum_neighbour_p2	...	rival_dif_neighbour_vsc	rival_dif_neighbour_p1	rival_dif_neighbour_p2	rival_dif_neighbour_sasa	rival_dif_neighbour_ncisc	rival_dif_neighbour_carbon	rival_dif_neighbour_hydrogen	rival_dif_neighbour_nitrogen	rival_dif_neighbour_oxygen	rival_dif_neighbour_sulfur
0	606.74	534.69	8.46	36.42	10.53	26.61	-0.52	390.3	32.7	0.910	...	-2.1	1.3	-3.000000e-02	0.132	-0.066372	-3.0	4.0	0.0	-1.0	1.0
1	497.60	425.53	9.15	37.97	12.48	28.68	0.25	275.5	30.4	0.617	...	77.5	2.4	2.450000e-01	1.379	0.036400	3.0	7.0	3.0	0.0	0.0
2	528.57	456.50	8.75	38.49	7.90	17.93	0.50	267.0	36.1	0.582	...	0.0	0.3	5.551115e-17	-0.011	0.035954	0.0	0.0	0.0	0.0	0.0
3	441.50	369.42	9.21	38.02	0.00	23.60	1.31	201.7	32.5	0.412	...	40.3	-6.5	5.200000e-02	0.299	-0.169404	2.0	5.0	-1.0	-1.0	0.0
4	441.50	369.42	9.21	38.02	0.00	23.60	1.31	201.7	32.5	0.412	...	40.3	-6.5	5.200000e-02	0.299	-0.169404	2.0	5.0	-1.0	-1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11665	489.55	417.50	8.55	37.83	8.18	22.18	0.71	226.1	31.5	0.510	...	-78.3	-2.1	-1.300000e-01	-0.706	-0.059006	-4.0	-7.0	-1.0	0.0	1.0
11666	467.53	395.47	8.71	38.69	0.00	23.73	1.34	221.6	32.9	0.497	...	20.4	-6.3	-3.300000e-02	-0.132	-0.216105	0.0	3.0	-1.0	-1.0	0.0
11667	539.62	467.56	8.16	37.85	14.18	24.05	0.17	275.8	32.4	0.678	...	28.6	0.6	-3.800000e-02	0.100	0.104364	1.0	5.0	-1.0	1.0	-1.0
11668	544.63	472.58	8.47	37.74	20.66	27.20	-1.94	279.8	33.5	0.693	...	-73.2	0.7	-1.690000e-01	-1.089	-0.131809	-4.0	-10.0	-2.0	1.0	1.0
11669	626.76	554.70	9.68	37.65	12.48	28.63	0.74	415.5	27.0	1.026	...	-62.5	5.8	-1.640000e-01	-0.268	0.040983	-6.0	0.0	2.0	0.0	0.0

Read data¶

Process data¶

Train-test splits¶

Train model¶